摘要： 为了提高关联规则挖掘算法处理大数据集的性能，提出一种新的模糊加权关联规则挖掘算法——FWAR算法。通过建立模糊加权关联规则模型生成候选项目集，并进行剪枝，新建的模型按权值对项目进行排序，符合向下封闭性，并解决了已有挖掘算法计算量大的问题。仿真结果证明通过该算法得到解的质量和计算速度有显著的提高。

关键词: 数据挖掘, 模糊加权关联规则, FWAR算法, 向下封闭性

Abstract: In order to advance the performance of association rules mining algorithm when disposing big dataset, a novel fuzzy weighted association rules mining algorithm namely FWAR is proposed. A new fuzzy weighted association rules model is built to make the candidate itemset and prune it. The model orders items by their weights, so it can satisfies the downward closure character and solve the big calculation problem in other mining algorithms. Simulation results demonstrate the scheme enhances the quality of the results and speed of computation distinctly.

Key words: data mining, fuzzy weighted association rules, FWAR algorithm, downward closure character

中图分类号: 

• TP391